MovieLens Recommendation System
A sophisticated recommendation engine built on the MovieLens dataset that provides personalized movie suggestions using collaborative filtering and matrix factorization techniques.
recommendation systems collaborative filtering python machine learning matrix factorization
MovieLens Recommendation System
This project implements a comprehensive movie recommendation system using the MovieLens dataset. The system analyzes user preferences and movie characteristics to deliver personalized movie suggestions, improving content discovery and user engagement.
Key Features
- Collaborative Filtering: Leverages user-user and item-item collaborative filtering approaches
- Matrix Factorization: Implements Singular Value Decomposition (SVD) for latent factor modeling
- Content-Based Filtering: Incorporates movie metadata (genres, directors, actors) for hybrid recommendations
- Cold Start Handling: Special handling for new users and new movies with limited interaction data
- A/B Testing Framework: System for evaluating recommendation quality and user satisfaction
Tech Stack
- Python
- Pandas
- NumPy
- Scikit-learn
- Surprise Library
- TensorFlow Recommenders
- Flask API
Model Performance
The system achieved a root mean square error (RMSE) of 0.86 on predicted ratings, with a precision@10 of 0.73 and recall@10 of 0.68, outperforming baseline popularity-based recommendations by 37%.
Implementation Highlights
- Hybrid Approach: Combined collaborative filtering with content-based methods for improved accuracy
- Scalability Solutions: Implemented efficient algorithms suitable for large-scale deployment
- Diversity Promotion: Balanced recommendation relevance with diversity to avoid filter bubbles
- Explanation Component: Added reasoning for recommendations to increase user trust and engagement
- Real-time Updates: System design allows for continuous model updating as new ratings are collected
User Experience
The recommendation system includes an intuitive user interface that allows users to:
- Rate movies they've watched
- Receive personalized recommendations
- See explanation for why items were recommended
- Filter recommendations by genre, release year, or other attributes
- Discover similar movies to ones they've enjoyed
Future Enhancements
- Deep learning-based recommendation models
- Contextual awareness (time of day, device, mood)
- Integration with external review and rating platforms
- Seasonality and trending content boosting